1 citations · 1 across the 6 of their papers we have counts for
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Unified Generalization for Frequency-Domain Channel Extrapolation Across Near-Field and Far-Field Scenarios
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
As antenna arrays grow, near-field effects become non-negligible in large-scale MIMO, making accurate low-overhead channel acquisition crucial in both far-field and near-field regi…
Physics-Informed Path-Parametric Learning for Efficient and Lightweight CSI Feedback
Chunyu Ling, Jiajia Guo, Yiming Cui +4
Channel State Information (CSI) feedback is vital for high spectral efficiency in wireless systems, yet high-dimensional CSI introduce significant feedback overhead. Recent deep le…
Generalizable Learning for Massive MIMO CSI Feedback in Unseen Environments
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (F…
Enhancing Environment Generalizability for Deep Learning-Based CSI Feedback
Haoyu Wang, Shuangfeng Han, Xiaoyun Wang +1
Accurate and low-overhead channel state information (CSI) feedback is essential to boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIM…
Generalizable Learning for Frequency-Domain Channel Extrapolation under Distribution Shift
Haoyu Wang, Zhi Sun, Shuangfeng Han +2
Frequency-domain channel extrapolation is effective in reducing pilot overhead for massive multiple-input multiple-output (MIMO) systems. Recently, Deep learning (DL) based channel…
AI-driven 6G Air Interface: Technical Usage Scenarios and Balanced Design Methodology
Xiaoyun Wang, Shuangfeng Han, Zhiming Liu +3
This paper systematically analyzes the typical application scenarios and key technical challenges of AI in 6G air interface transmission, covering important areas such as performan…